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Model comparison

GLM-4.7 vs Inkling-Small

Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

GLM-4.7

Z.AI

60.4/100

Supported · Public rank #46

90% interval 46.7–74.0

Inkling-Small

Thinking Machines Lab

Evidence status unavailable

90% interval unavailable

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.

5 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Inkling-Small

    Inkling-Small leads on the same 2 weighted benchmark rows.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    Inkling-Small

    Inkling-Small has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls
  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GLM-4.7 does not fit this workload in one request. GLM-4.7 has no comparable published API token rate.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
5
GLM-4.7 only
8
Inkling-Small only
15
Like-for-like categories
1 / 8

2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Like-for-like
GLM-4.7
45.7
Inkling-Small
70.1
Weighted basis
2 vs 2 rows
Reading
Inkling-Small leads

Coding

Directional only
GLM-4.7
75.4
Inkling-Small
62.4
Weighted basis
3 vs 3 rows
Reading
Directional only

Knowledge

Directional only
GLM-4.7
51.8
Inkling-Small
53.4
Weighted basis
3 vs 2 rows
Reading
Directional only

Reasoning

Not comparable
GLM-4.7
Not measured
Inkling-Small
40.1
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
GLM-4.7
1.8
Inkling-Small
92.9
Weighted basis
2 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
GLM-4.7
Not measured
Inkling-Small
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GLM-4.7
Not measured
Inkling-Small
76.6
Weighted basis
0 vs 2 rows
Reading
Not comparable

Instruction following

Not comparable
GLM-4.7
Not measured
Inkling-Small
82.2
Weighted basis
0 vs 1 rows
Reading
Not comparable

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

GLM-4.7
Self-hosted; infrastructure cost varies
Fits in one request
Inkling-Small
$0.0013
Fits in one request

GLM-4.7 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GLM-4.7
Self-hosted; infrastructure cost varies
Fits in one request
Inkling-Small
$0.03332
Fits in one request

GLM-4.7 has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

GLM-4.7
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable
Inkling-Small
$0.0492
Fits in one request

GLM-4.7 does not fit this workload in one request. GLM-4.7 has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Context window

Maximum documented context; output-token limits may be lower.

GLM-4.7

200K

Inkling-Small

1M

API model ID

GLM-4.7

Not sourced

Inkling-Small

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

GLM-4.7

No comparable hosted API rate

Inkling-Small

$0.116 per 1M cached input tokens

Documented inputs

GLM-4.7

Not sourced

Inkling-Small

Not sourced

Documented outputs

GLM-4.7

Not sourced

Inkling-Small

Not sourced

Provider availability

GLM-4.7

Not sourced

Inkling-Small

Not sourced

Reasoning profile

GLM-4.7

Reasoning

Inkling-Small

Hybrid

Weight access

GLM-4.7

Open Weight

Inkling-Small

Open Weight

License

GLM-4.7

Open Weight

Inkling-Small

Open Weight

Release date

GLM-4.7

2025-10-01

Inkling-Small

2026-07-30

If you already use one of these models
Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Inkling-Small has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence28 rows

Agentic

  • Terminal-Bench 2.0

    GLM-4.741%
    Source
    Inkling-Small64.7%
    Source

    Inkling-Small leads this result

  • BrowseComp

    GLM-4.752%
    Source
    Inkling-Small77.4%
    Source

    Inkling-Small leads this result

  • VITA-Bench

    GLM-4.715.5%
    Source
    Inkling-Small

    Not directly comparable

  • Gert Labs

    GLM-4.739.95%
    Source
    Inkling-Small

    Not directly comparable

  • MCP Atlas

    GLM-4.7
    Inkling-Small79.6%
    Source

    Not directly comparable

  • Toolathlon-Verified

    GLM-4.7
    Inkling-Small54.4%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    GLM-4.773.8%
    Source
    Inkling-Small80.2%
    Source

    Inkling-Small leads this result

  • LiveCodeBench

    GLM-4.784.9%
    Source
    Inkling-Small

    Not directly comparable

  • SWE-Rebench

    GLM-4.758.7%
    Source
    Inkling-Small

    Not directly comparable

  • SWE-bench Pro

    GLM-4.7
    Inkling-Small55.9%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    GLM-4.7
    Inkling-Small64.7%
    Source

    Not directly comparable

  • SciCode

    GLM-4.7
    Inkling-Small48.7%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GLM-4.7
    Inkling-Small40.1%
    Source

    Not directly comparable

  • CritPt

    GLM-4.7
    Inkling-Small8.3%
    Source

    Not directly comparable

Knowledge

  • GPQA

    GLM-4.785.7%
    Source
    Inkling-Small89.5%
    Source

    Inkling-Small leads this result

  • MMLU-Pro

    GLM-4.784.3%
    Source
    Inkling-Small

    Not directly comparable

  • HLE

    GLM-4.724.8%
    Source
    Inkling-Small47.8%
    Source

    Inkling-Small leads this result

  • GPQA-D

    GLM-4.7
    Inkling-Small89.5%
    Source

    Not directly comparable

  • HLE w/o tools

    GLM-4.7
    Inkling-Small31.6%
    Source

    Not directly comparable

Math

  • AIME 2025

    GLM-4.795.7%
    Source
    Inkling-Small

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GLM-4.72.439%
    Source
    Inkling-Small

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GLM-4.70.000%
    Source
    Inkling-Small

    Not directly comparable

  • AIME26

    GLM-4.7
    Inkling-Small95.5%
    Source

    Not directly comparable

  • HMMT Feb 2026

    GLM-4.7
    Inkling-Small90.2%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    GLM-4.7
    Inkling-Small74%
    Source

    Not directly comparable

  • CharXiv

    GLM-4.7
    Inkling-Small81.3%
    Source

    Not directly comparable

  • CharXiv w/o tools

    GLM-4.7
    Inkling-Small77.4%
    Source

    Not directly comparable

Instruction following

  • IFBench

    GLM-4.7
    Inkling-Small82.2%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GLM-4.7 or Inkling-Small?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, GLM-4.7 or Inkling-Small?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, GLM-4.7 or Inkling-Small?

Inkling-Small leads the like-for-like agentic tasks comparison across 2 shared weighted benchmark rows.

Which costs less, GLM-4.7 or Inkling-Small?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, GLM-4.7 or Inkling-Small?

Inkling-Small has the larger documented context window: 1M, compared with 200K.

Related comparisons

Last updated July 30, 2026

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